1. Technical Field
The invention is related to computer systems for constructing and rendering panoramic mosaic images from a sequence of still images, video images or scanned photographic images or the like.
2. Background Art
Image-based rendering is a popular way to simulate a visually rich tele-presence or virtual reality experience. Instead of building and rendering a complete 3D model of the environment, a collection of images is used to render the scene while supporting virtual camera motion. For example, a single cylindrical image surrounding the viewer enables the user to pan and zoom inside an environment created from real images. More powerful image-based rendering systems can be built by adding a depth map to the image or by using a larger collection of images.
The present invention is particularly directed to image-based rendering systems without any depth information, i.e., those which only support user panning, rotation, and zoom. Most of the commercial products based on this idea (such as QuickTime VR) use cylindrical images with a limited vertical field of view, although newer systems support full spherical maps (e.g., Interactive Pictures, and RealVR). A number of techniques have been-developed for capturing panoramic images of real-world scenes. One way is to record an image onto a long film strip using a panoramic camera to directly capture a cylindrical panoramic image. Another way is to use a lens with a very large field of view such as a fisheye lens. Mirrored pyramids and parabolic mirrors can also be used to directly capture panoramic images. A less hardware-intensive method for constructing full view panoramas is to take many regular photographic or video images in order to cover the whole viewing space. These images must then be aligned and composited into complete panoramic images using an image mosaic or "stitching" method. Most stitching systems require a carefully controlled camera motion (pure pan), and only produce cylindrical images.
Cylindrical panoramas are commonly used because of their ease of construction. To build a cylindrical panorama, a sequence of images is taken by a camera mounted on a leveled tripod. If the camera focal length or field of view is known, each perspective image can be warped into cylindrical coordinates. To build a cylindrical panorama, 3D world coordinates are mapped to 2D cylindrical screen coordinates using ##EQU1## where .theta. is the panning angle and .nu. is the scanline. Similarly, 3D world coordinates can be mapped into 2D spherical coordinates .theta., .phi. using ##EQU2##
Once each input image has been warped, constructing the panoramic mosaics for a leveled camera undergoing a pure panning motion becomes a pure translation problem. Ideally, to build a cylindrical or spherical panorama from a horizontal panning sequence, only the unknown panning angles need to be recovered. In practice, small vertical translations are needed to compensate for vertical jitter and optical twist. Therefore, both a horizontal translation t.sub.x and a vertical translation t.sub.y are estimated for each input image. To recover the translational motion, the incremental translation .delta.t=(.delta.t.sub.x,.delta.t.sub.y) is estimated by minimizing the intensity error between two images, I.sub.0, I.sub.1. ##EQU3## where EQU x.sub.i =(x.sub.i,y.sub.i) and x'.sub.i =(x'.sub.i,y'.sub.i)=(x.sub.i +t.sub.x,y.sub.i +t.sub.y)
are corresponding points in the two images, and t=(t.sub.x,t.sub.y) is the global translational motion field which is the same for all pixels. After a first order Taylor series expansion, the above equation becomes ##EQU4## where e.sub.i =I.sub.1 (x'.sub.1)-I.sub.0 (x.sub.i) is the current intensity or color error, and g.sub.i.sup.T =.gradient.I.sub.1 (x'.sub.1) is the image gradient of I.sub.1 at x'.sub.i. This minimization problem has a simple least-squares solution, ##EQU5## To handle larger initial displacements, a hierarchical coarse-to-fine optimization scheme is used. To reduce discontinuities in intensity and color between the images being composited, a simple feathering process is applied in which the pixels in each image are weighted proportionally to their distance to the edge (or more precisely, their distance to the nearest invisible pixel). Once registration is finished, the ends are clipped (and optionally the top and bottom), and a single panoramic image is produced.
Creating panoramas in cylindrical or spherical coordinates has several limitations. First, it can only handle the simple case of pure panning motion. Second, even though it is possible to convert an image to 2D spherical or cylindrical coordinates for a known tilting angle, ill-sampling at north pole and south pole causes big registration errors. (Note that cylindrical coordinates become undefined as you tilt your camera toward north or south pole.) Third, it requires knowing the focal length (or equivalently, field of view). While focal length can be carefully calibrated in the lab, estimating the focal length of the lens by registering two or more images using conventional techniques is not very accurate.
The automatic construction of large, high-resolution image mosaics is an active area of research in the fields of photogrammetry, computer vision, image processing, and computer graphics. Image mosaics can be used for many different applications. The most traditional application is the construction of large aerial and satellite photographs from collections of images. More recent applications include scene stabilization and change detection, video compression and video indexing, increasing the field of view and resolution of a camera, and even simple photo editing. A particularly popular application is the emulation of traditional film-based panoramic photography with digital panoramic mosaics, for applications such as the construction of virtual environments and virtual travel. In computer vision, image mosaics are part of a larger recent trend, namely the study of visual scene representations. The complete description of visual scenes and scene models often entails the recovery of depth or parallax information as well.
In computer graphics, image mosaics play an important role in the field of image-based rendering, which aims to rapidly render photorealistic novel views from collections of real (or pre-rendered) images. For applications such as virtual travel and architectural walkthroughs, it is desirable to have complete (full view) panoramas, i.e., mosaics which cover the whole viewing sphere and hence allow the user to look in any direction. Unfortunately, most of the results to date have been limited to cylindrical panoramas obtained with cameras rotating on leveled tripods with carefully designed stages adjusted to minimize motion parallax. This has limited the users of mosaic building ("'stitching") to researchers and professional photographers who can afford such specialized equipment. Present techniques are difficult because generally they require special camera equipment which provides pure panning motion with no motion parallax.
Problems to be Solved by the Invention:
It would be desirable for any user to be able to "paint" a full view panoramic mosaic with a simple hand-held camera or camcorder. However, this requires several problems to be overcome.
First, cylindrical or spherical coordinates should be avoided for constructing the mosaic, since these representations introduce singularities near the poles of the viewing sphere.
Second, accumulated misregistration errors need to be corrected, which are always present in any large image mosaic. For example, if one registers a sequence of images using pairwise alignments, there is usually a gap between the last image and the first one even if these two images are the same. A simple "gap closing" technique is introduced in the specification below which forces the first and last image to be the same and distributes the resulting corrections across the image sequence. Unfortunately, this approach works only for pure panning motions with uniform motion steps, a significant limitation.
Third, any deviations from the pure parallax-free motion model or ideal pinhole (perspective) camera model may result in local misregistrations, which are visible as a loss of detail or multiple images (ghosting).